Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R

David Bellot, Dan Toomey

Probabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models.We’ll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we’ll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you’ll see the advantage of going probabilistic when you want to do prediction. Next, you’ll master using R packages and implementing its techniques. Finally, you’ll be presented with machine learning applications that have a direct impact in many fields. Here, we’ll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems.

Learning PySpark. Click here to enter text

Tomasz Drabas, Denny Lee

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.

Learning Python Design Patterns. - Second Edition

Chetan Giridhar, Gennadiy Zlobin, Anand Balachandran Pillai

With the increasing focus on optimized software architecture and design it is important that software architects think about optimizations in object creation, code structure, and interaction between objects at the architecture or design level. This makes sure that the cost of software maintenance is low and code can be easily reused or is adaptable to change. The key to this is reusability and low maintenance in design patterns.Building on the success of the previous edition, Learning Python Design Patterns, Second Edition will help you implement real-world scenarios with Python’s latest release, Python v3.5. We start by introducing design patterns from the Python perspective. As you progress through the book, you will learn about Singleton patterns, Factory patterns, and Façade patterns in detail. After this, we’ll look at how to control object access with proxy patterns. It also covers observer patterns, command patterns, and compound patterns.By the end of the book, you will have enhanced your professional abilities in software architecture, design, and development.

Learning Qlik Sense: The Official Guide. Get the most out of your Qlik Sense investment with the latest insight and guidance direct from the Qlik Sense team - Second Edition

Christopher Ilacqua, QlikTech International AB, Henric Cronström, James Richardson

The intuitive and powerful Qlik Sense visual analytics software allows anyone to engage in data discovery, to explore your data, and find meaningful insights to empower your business. Qlik Sense lets you easily create personalized reports and visualizations and reveal essential connections to show new opportunities from every angle.Written by members of the Qlik Sense team, this book is the official guide from Qlik to understanding and using their powerful new product with fully updated coverage to the latest features of the most modern edition of Qlik Sense. Benefit from the vision behind the development of Qlik Sense and get to grips with how Qlik Sense can empower you as a data discovery consumer. Learn how to create your own applications for Qlik Sense to customize it to meet your personal needs for business intelligence, and how to oversee and administer the Qlik Sense data architecture. Finally, explore utilizing Qlik Sense to uncover essential data, with practical examples on finding and visualizing intelligence for sales figures, human resources information, travel expense tracking, and demographic data discovery.

Learning Redis. Design efficient web and business solutions with Redis

Vinoo Das

Learning SAP Analytics Cloud. Collaborate, predict and solve business intelligence problems with cloud computing

Riaz Ahmed

The book starts with the basics of SAP Analytics Cloud (formerly known as SAP BusinessObjects Cloud) and exposes almost every significant feature a beginner needs to master. Packed with illustrations and short, essential, to-the-point descriptions, the book provides a unique learning experience. Your journey of exploration starts with a basic introduction to the SAP Analytics Cloud platform. You will then learn about different segments of the product, such as Models, Stories, Digital Boardroom, and so on. Then, you are introduced to the product's interface: the Home screen, the main menu, and more. Then comes the hands-on aspect of the book, which starts with model creation. Next, you learn how to utilize a model to prepare different types of stories(reports) with the help of charts, tables, Geo Maps, and more. In the final chapters of this book, you will learn about Digital Boardroom, Collaboration, and Administration.

Learning Social Media Analytics with R. Transform data from social media platforms into actionable business insights

Dipanjan Sarkar, Raghav Bali, Tushar Sharma

The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data.The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights.

Learning Spark SQL. Architect streaming analytics and machine learning solutions

Aurobindo Sarkar

In the past year, Apache Spark has been increasingly adopted for the development of distributed applications. Spark SQL APIs provide an optimized interface that helps developers build such applications quickly and easily. However, designing web-scale production applications using Spark SQL APIs can be a complex task. Hence, understanding the design and implementation best practices before you start your project will help you avoid these problems.This book gives an insight into the engineering practices used to design and build real-world, Spark-based applications. The book's hands-on examples will give you the required confidence to work on any future projects you encounter in Spark SQL.It starts by familiarizing you with data exploration and data munging tasks using Spark SQL and Scala. Extensive code examples will help you understand the methods used to implement typical use-cases for various types of applications. You will get a walkthrough of the key concepts and terms that are common to streaming, machine learning, and graph applications. You will also learn key performance-tuning details including Cost Based Optimization (Spark 2.2) in Spark SQL applications. Finally, you will move on to learning how such systems are architected and deployed for a successful delivery of your project.